无金标准的网络的贝叶斯集成。
Bayesian integration of networks without gold standards.
机构信息
Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
出版信息
Bioinformatics. 2012 Jun 1;28(11):1495-500. doi: 10.1093/bioinformatics/bts154. Epub 2012 Apr 6.
MOTIVATION
Biological experiments give insight into networks of processes inside a cell, but are subject to error and uncertainty. However, due to the overlap between the large number of experiments reported in public databases it is possible to assess the chances of individual observations being correct. In order to do so, existing methods rely on high-quality 'gold standard' reference networks, but such reference networks are not always available.
RESULTS
We present a novel algorithm for computing the probability of network interactions that operates without gold standard reference data. We show that our algorithm outperforms existing gold standard-based methods. Finally, we apply the new algorithm to a large collection of genetic interaction and protein-protein interaction experiments.
AVAILABILITY
The integrated dataset and a reference implementation of the algorithm as a plug-in for the Ondex data integration framework are available for download at http://bio-nexus.ncl.ac.uk/projects/nogold/
动机
生物实验可以深入了解细胞内部的过程网络，但实验结果会受到误差和不确定性的影响。然而，由于公共数据库中报告的大量实验存在重叠，因此可以评估单个观察结果的正确性。为了做到这一点，现有的方法依赖于高质量的“黄金标准”参考网络，但并非总是有这样的参考网络。
结果
我们提出了一种新的算法，用于计算网络相互作用的概率，该算法无需黄金标准参考数据即可运行。我们表明，我们的算法优于现有的基于黄金标准的方法。最后，我们将新算法应用于大量的遗传相互作用和蛋白质-蛋白质相互作用实验。
可用性
集成数据集和作为 Ondex 数据集成框架插件的算法的参考实现可在 http://bio-nexus.ncl.ac.uk/projects/nogold/ 下载。
Zotero 插件